metrics<-function(z){Q75=quantile(z,probs=c(0.75))

aboveq75=sum(z>Q75)

dh75=aboveq75/length(z)

return(dh75)}

DH75<-grid_metrics(lid,~metrics(Z),res = 30)

> metrics(runif(1000))
[1] 0.25
> metrics(runif(12))
[1] 0.25
> metrics(runif(13))
[1] 0.2307692

LASfile <- system.file("extdata", "Megaplot.laz", package="lidR")
las = readLAS(LASfile)
Q75=quantile(las$Z,0.75)
Q75
##   75%
## 19.32

metrics <-
  function(z){
   aboveq75=sum(z>Q75)
   dh75=aboveq75/length(z)
   return(dh75)
  }

> DH75<-grid_metrics(las,~metrics(Z),res = 3)
> plot(DH75)

#dependencies
library(lidR)
library(ggplot2)
library(stats)

#specify file path
infile <- 'path_to_your_cloud.las'

#read in las cloud
las <- readLAS(infile)

#create density raster
density_plot <- grid_density(las, res = 10)

#calculate 75th percentile point density
Q75 = quantile(density_plot$point_density, probs = 0.75)

#filter raster based on value
density_plot_Q75 <- density_plot[density_plot$point_density > Q75]

#plot unfiltered raster
ggplot() +
  geom_raster(data = density_plot , aes(x = X, y = Y, fill = point_density)) +
  coord_quickmap()

#plot filtered raster
ggplot() +
  geom_raster(data = density_plot_Q75 , aes(x = X, y = Y, fill = point_density)) +
  coord_quickmap()